Informetrics

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Informetrics IR

• Presentation
• Readings Discussion Review
• Projects Papers

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Why use metrics?

• Apply theory from another field to solve IS
  problems
• We need new modeling techniques or metaphors to
  examine these complex systems
• An attempt to apply some new models and metaphors
  to complex systems
• Bibliometrics
• Direct Citation Counting
• Bib Coupling
• Co-Citation Analysis
• Bibliometric Laws
• Web Servers
• Server Log
• Log Analysis

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How do Informetrics impact IR?

• Measures of
• Content subject area
• Relationships
• Use popularity
• An information-based view of communications,
  focused on documents
• Instead of the text in a document, focus on the
  document properties (metadata?)
• Author(s)
• Dates
• Publication source(s)
• Front Matter Titles Contact info
• Back Matter Citations Support

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What are these metrics?

• Bibliometrics
• series of techniques that seek to quantify the
  process of written communication. Ikpaahindi
• counting and analyzing citations
• consistently observable patterns
• referenced in key places Science Citation Index,
  Social Science Citation Index, Arts and
  Humanities Citation Index
• Webometrics
• Applying bibliometric methods to Web pages Web
  sites
• Informetrics
• Wider scale application of methods to networked
  information sources

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Citing Linking

• paying homage to pioneers
• giving credit for related work (homage to peers)
• identifying methodology, equipment, etc.
• background reading
• correcting ones own work
• correcting the work of others
• criticizing previous work
• substantiating claims
• alerting to forthcoming work
• providing leads to poorly disseminated, poorly
  indexed, or un-cited work
• authenticating data and classes of fact -
  physical constants, etc.
• identifying original pubs in which an idea or
  concept was discussed
• id original pub or other work describing an
  eponymic concept or term (Hodgkins Disease)
• disclaiming work or ideas of others (negative
  claims)
• disputing priority claims of others (negative
  homage)

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Direct Citation Counting

• How many citations over a given period of time.
• Impact formula
• n journal citations/n citable articles published
• Immediacy index
• n citations received by article during the year/
  total number of citable articles published

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Bibliometric Coupling

• a number of papers bear a meaningful relation
  to each other when they have one or more
  references in common Kessler
• Whats the Web equivalent?

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Co-Citation Analysis

• if two references are cited together, in a latter
  literature, the two references are themselves
  related. the greater the number of times they are
  cited together, the greater their cocitation
  strength. (Marshakova and Small 1973
  independently)
• How about Web citations?
• Whats a set of Web pages? A Site, a long page?

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Finer Points

• Classification of references
• is the reference conceptual or operational
• is the reference organic or perfunctory
• is the reference evolutionary or juxtapositional
  (built on a preceding or an alternative to it)
• is the reference confirmative or negational
• Citation reference errors
• multiple authors (not primary or et. al.) what
  contribution/influence by order of names?
• self-citations
• like-names, initial/full names, different fields
• field variation of citation amounts/purposes
• fluctuation of influence/use
• typos

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Bibliometric Laws

• Seek to describe the working of science by
  mathematical means. Generally that a few entities
  account for the many citations.
• Bradfords Law of Scattering
• Lotkas Law
• Zipfs Law

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Bradfords Law of Scattering

• How literature in a subject in distributed in
  journals.
• If scientific journals are arranged in order of
  decreasing productivity of articles on a given
  subject, they may be divided into a nucleus of
  periodicals more particularly devoted to the
  subject and several other groups of zones
  containing the same number of articles as the
  nucleus.
• 9 journals had 429 articles, the next 59 had 499,
  the last 258 had 404.
• Bradford discovered this regularity of
  calculating the number of titles in each of the
  three groups 9 titles, 9x5 titles, 9x5x5 titles.
  
• Can be influenced by sample size, area of
  specialization and journal policies.

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Brookes on Bradfords Formula

• The index terms assigned to documents also
  follow a Bradford distribution because those
  terms most frequently assigned become less and
  less specific and therefore increasingly
  ineffective in retrieval.

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Bradfords Formula Itself

• Bradfords Formula makes it possible to estimate
  how many of the most productive sources would
  yield any specified fraction p of the total
  number of items. The formula is
• R(n) N log n/s (1 lt_ n lt_ N)
• where R(n) cumulative total of items
  contributed by the sources of rank 1 to n.
• N total number of contributing sources
• s a constant characteristic of the literature
• then
• R(N) N log N/s
• is the total number of items contributed by N
  sources.

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More Bradfords Law

• Citations originally counted year by year can be
  expressed as the geometric sequence
• R, Ra, Ra2, Ra3, Ra4, ..., Rat-1
• where R presumed number of citations during the
  first year, some of which do not immediately
  emerge in publication. But as alt1, the sum of the
  sequence converges to the finite limit R/(1-a).

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Lotkas Law

• An inverse square law that for every 100 authors
  contributing on article, 25 will contribute 2, 11
  will contribute 3 and 6 will contribute 4.
• formula is- 1n2.
• Voos found 1n3.5 for Info Science (1974).
• What are other similar analysis tasks you could
  use Lotkas law for?
• Are users, browsers, bloggers like authors?

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Zipfs Law

• The distribution which applied to word frequency
  in a text states that the nth ranking word will
  appear k/n times, where k is a constant for that
  text.
• It is easier to choose and use familiar words,
  therefore probabilities of occurrence of familiar
  words is higher. rfC rank, frequency,
• This can be applied by counting all of the words
  in a document (minus some words in a stop list -
  common words (the, therefore...)) with the most
  frequent occurrences representing the subject
  matter of the document. Could also use relative
  frequency (more often than expected) instead of
  absolute frequency.

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Wyllys on Zipfs Law

• Surprisingly constrained relationship between
  rank and frequency in natural language.
• Zipf said the fundamental reason for human
  behavior the striving to minimize effort.
• Mandelbrot - further refinement of Zipfs law
  (rm)Bfc where r is the rank of a word, f is its
  frequency, m, B and c are constants dependent on
  the corpus. m has the greatest effect when r is
  small.

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Optimum utility of articles?

• the most compact library is not the least costly
  because you get rid of articles more quickly
  therefore you buy more.
• fewer articles are acquired and kept longer but
  more shelf space and maintenance is needed.
• the challenge is to keep the most frequently
  accessed available.

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Goffmans Theory

• His General Theory of Information Systems
• Ideas are endemic with minor outbreaks
  occurring from time to time. Cycles of use. Like
  memes and paradigm shifts (Kuhn). Based on
  epidemiology and Shannons communications theory.

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Online Article Life

• Burton proposed a measure for the decay in
  citations to older literature, a half-life
• How is this different on the net?
• a shorter life?
• older sites referred less, more?
• commercial sites vs. private sites.
• advertised vs word of mouth?
• linked from popular pages?

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Prices Law

• half of the scientific papers are contributed by
  the square root of the total number of scientific
  authors
• Leads to
• bibliographic coupling - the number of reference
  two papers have in common, as a measure of their
  similarity, a clustering based on this measure
  yields meaningful groupings of papers for
  information retrieval.

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Cumulative advantage model

• Price noticed this advantage
• Success breeds success. also implies that an
  obsolescence factor is at work. You get mentioned
  a lot, you get mentioned in more and more cited
  papers.
• Polya describes this as contagion

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Bibliometrics on the Web

• We can use these techniques, rules and formulas
  to analyze Web usage.
• Like a bibliometric index for historical
  analysis.
• Key question are citations like page
  browsing/using?
• Using Web Servers Effectively
• Server Logs give us much data to mine
• Studies on the Web

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Understanding the Web

• User-based data collection
• Surveys
• GVU, Nielsen and GNN
• Qualitative questions
• phone
• web forms
• Self-selected sample problems
• random selection
• oversample

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Understanding the Web

• Web Servers
• Serve
• text
• graphics
• CGI
• XMLHTTPRequest (REST, AJAX)
• Web services (SOAP)
• other MIME types
• Server Logs represent this activity
• A lot of empirical, quantitative data on use

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Problems with Web Servers

• Not as Foolproof as Print
• No State Information
• Interaction with Web pages or Web apps is
  difficult to log analyze
• Server Hits not Representative
• Counters inaccurate
• Different, non HTTP requests effects
• Floods/Bandwidth can Stop intended usage
• Robots, Spam, (D)DoS, Caching, etc.

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Web Server Records

• Server-based
• Proxy-based
• Client-based
• Network-based

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Clever Web Content Setup

• unique file and directory names
• clear, consistent structure
• FTP server for file transfer
• frees up logs and server!
• Judicious use of links
• Wise MIME types
• some hard/impossible to log

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Clever Web Server Setup

• Redirect CGI to find referrer
• Use a database
• store web content
• record usage data
• create state information with programming
• NSAPI
• ActiveX
• Have contact information
• Have purpose statements
• Bibliometric Servlets?

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Managing Log Files

• Backup
• Store Results or Logs?
• Beginning New Logs
• Posting Results

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Log File Format

• see Appendix
• key advantage
• computer storage cost decreases while paper cost
  rises
• every server generates slightly different logs

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Extended Log File Formats

• WWW Consortium Standards
• Will automatically record much of what is
  programmatically done now.
• faster
• more accurate
• standard baselines for comparison
• graphics standards

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Log Analysis Tools

• Analog
• WWWStat
• GetStats
• Perl Scripts
• Commercial Tools

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Log Analysis Cumulative Sample

• Program started at Tue-03-Dec-2006 0120 local
  time.
• Analysed requests from Thu-28-Jul-2003 2031 to
  Mon-02-Dec-2003 2359 (858.1 days).
• Total successful requests 4 282 156 (88 952)
• Average successful requests per day 4 990 (12
  707)
• Total successful requests for pages 1 058 526
  (17 492)
• Total failed requests 88 633 (1 649)
• Total redirected requests 14 457 (197)
• Number of distinct files requested 9 638 (2 268)
  
• Number of distinct hosts served 311 878 (11 284)
  
• Number of new hosts served in last 7 days 7 020
• Corrupt logfile lines 262
• Unwanted logfile entries 976
• Total data transferred 23 953 Mbytes (510 619
  kbytes)
• Average data transferred per day 28 582 kbytes
  (72 946 kbytes)

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Downie and Web Usage

• User-based analyses
• who
• where
• what
• File-based analyses
• amount
• Request analyses
• conform (loosely) to Zipfs Law
• Byte-based analyses

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Neat Bibliometric Web Tricks

• use a search engine to find references
• linkwww.ischool.utexas/donturn
• key to using unique names
• use many engines
• update times different
• blocking mechanisms are different
• use Google News (and the like)
• look for references
• look for IP addresses of users

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Neat Tricks, cont.

• Walking up the Links
• follow URLs upward
• Reverse Sort
• look for relations
• Use your own robot to index
• test

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Projects

• capture current and previous user information
  seeking behavior and modify interface and content
  to meet needs
• Dynamic Web Publishing System
• anticipate information seeking behavior
• based on recorded preferences and pre-supplied
  rules, generate and guide users through a
  document space.

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Summary

• Bibliometrics, now Informetrics
• Bradfords - distribution of documents in a
  specific discipline
• Lotkas - number of authors of varying
  productivity
• Zipfs - word frequency rankings
• The Web
• out of control in growth opportunities
• wise setup can help
• use good analysis tools

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Projects Papers

• Everyone have topic or project?
• Lets talk more (via email too) about ideas and
  projects